I am new to this data science stuff and I am trying a project on my own to learn more about this field. So I have a project that has the goal of taking in a bunch of features and indicating whether a player will make or miss a shot.
My current training data has a bunch of features alongside the output for each observation. I plan on using a Random Forrest model, as I am comfortable with it (and it fits the objective), however, one issue I see includes making sure luck does not play a role in the decision of the output.
I am trying to think of ways to limit the impact of luck on the model. For anyone familiar with basketball, sometimes a player takes a great shot and misses- sometimes he takes a horrible shots and makes it (both of those situat will be included in my training set). I do not want the model "thinking" that a shot is good/bad because of lucky/unlucky makes/misses.
So my question is how can I limit the impact of the luck within my data-sets or am I just able to assume that a large enough data set will take care of the luck since one gets lucky and unlucky at relatively equal rates (normal distribution) or do I instead revert to an unsupervised model that has the test data not include whether the shot was a miss or a make? Or is there another option to do something I have not considered to make the data better?
Thank you for your feedback.